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I was browsing through the news today and I found an interesting article about the current state of AI for corporate productivity.
MIT report: 95% of generative AI pilots at companies are failing
There seems to have been a feeling over the last few years that generative AI was going to gut white collar jobs the same way that offshoring gutted blue collar jobs in the 1980s and 90s, and that it was going to happen any day now.
If this study is trustworthy, the promise of AI appears to be less concrete and less imminent than many would hope or fear.
I've been thinking about why that might be, and I've reached three non-exclusive but somewhat unrelated thoughts.
The first is that Gartner hype cycle is real. With almost every new technology, investors tend to think that every sigmoid curve is an exponential curve that will asymptotically approach infinity. Few actually are. Are we reaching the point where the practical gains available in each iteration our current models are beginning to bottom out? I'm not deeply plugged in to the industry, nor the research, nor the subculture, but it seems like the substantive value increase per watt is rapidly diminishing. If that's true, and there aren't any efficiency improvements hiding around the next corner, it seems like we may be entering the through of disillusionment soon.
The other thought that occurs to me is that people seem to be absolutely astounded by the capabilities of LLMs and similar technology.
Caveat: My own experience with LLMs is that it's like talking to a personable schizophrenic from a parallel earth, so take my ramblings with a grain of salt.
It almost seems like LLMs exist in an area similar to very early claims of humanoid automata, like the mechanical Turk. It can do things that seem human, and as a result, we naturally and unconsciously ascribe other human capabilities to them while downplaying their limits. Eventually, the discrepancy grows to great - usually when somebody notices the cost.
On the third hand, maybe it is a good technology and 95% of companies just don't know how to use it?
Does anyone have any evidence that might lend weight to any of these thoughts, or discredit them?
My experience as a senior software engineer is that I am not worried about AI coming for my job any time soon. My impression (somewhat bolstered by the article) is that AI is most efficient when it is starting from scratch and runs into issues when attempting to integrate into existing workflows. I tell the AI to write unit tests and it fails to do all the mocking required because it doesn't really understand the code flow. I ask it to implement a feature or some flow and it hallucinates symbols that don't exist (enum values, object properties, etc). It will straight up try to lie to me about how certain language features work. It's best utilized where there is some very specific monotonous change I need to make across a variety of files. Even then it sometimes can't resist making a bunch of unrelated changes along the way. I believe that if you are a greenfield-ish startup writing a ton of boilerplate to get your app off the ground, AI is probably great. If you are a mature product that needs to make very targeted changes requiring domain knowledge AI is much less helpful.
I can believe people using AI for different things are having very different experiences and each reporting their impressions accurately.
Partially, but there is also a honeymoon phase and a phenomenon where people feel more productive but have mostly just shifted what they do, not increased their actual productivity.
Perhaps this is something that will pass with increased experience with the tools but it has not been my experience with the people i manage nor for my friends in similar managerial roles. It could of course be a combination of the above as well. Maybe the models just need to get a bit better and people need experience with those models. Who knows?
To me it seems highly specific where AI actually is a meaningful productivity booster for programming. It should be clear though that for these things it is very valuable.
I would be more worried for areas where things don't actually have to be "correct" (for quality or legal reasons), like visual art generation. Even there I imagine things will mostly affect the things liable to be (or already has been) outsourced.
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